PREDICTIVE MODELING FOR CROP YIELD ESTIMATION: MACHINE LEARNING CLASSIFIER COMPARISION

Abstract

The aim is to enhance agricultural productivity through advanced predictive modeling. By leveraging machine learning techniques, the goal is to analyze and compare various classifiers to accurately estimate crop yields. Using historical data, weather patterns, soil conditions, and other relevant factors, a robust framework is to be developed for forecasting crop yields with high precision. Traditional methods of estimating crop yields often depend on manual observation and historical trends. These approaches are typically time-consuming, labor-intensive, and prone to errors due to the complex and dynamic nature of agricultural systems. Moreover, they often fail to fully utilize available data or adapt to changing environmental conditions, resulting in less accurate predictions. There is a clear need for a more efficient and accurate yield estimation system that takes advantage of machine learning algorithms. The focus is on overcoming the limitations of older methods by building predictive models capable of processing large datasets, recognizing patterns, and generating precise predictions based on variables such as weather, soil properties, and farming practices. The motivation stems from the potential impact on agricultural decision-making. Accurate predictions enable farmers to make better-informed choices regarding planting schedules, harvesting times, and resource distribution, ultimately improving productivity and optimizing resource use. By applying machine learning, the system offers valuable insights that support better crop management and contribute to sustainable agriculture and food security